Genetic algorithms based self-tuning regulator

Genetic algorithms (GAs) have been proven as robust search procedures. Numerous researchers have established the validity of GAs in optimization, machine learning, and control applications. The paper presents a new intelligent regulator with self tuning scheme using the robust search feature of GAs incorporated with the basic idea of self-tuning regulators. The proposed regulator utilizes GAs to search for the changes of system parameters and to calculate the corresponding control law. The optimum parameters and control law are chosen by means of the selection mechanism of GAs which employs the squares of difference between the actual and the estimated outputs as the fitness function. The regulator has an online parameter identification function and requires neither prior knowledge nor training data for learning. The proposed GA based system is applied to the load frequency control of a power system in order to investigate its effectiveness. As demonstrated by the results obtained from computer simulations, the intelligent regulator can provide good system characteristics